Biology subjects

Ghaffari, A.

Publications and source records attributed to Ghaffari, A..

5 recordsLinked to original sources

bioRxiv2025

Connectome-based predictive modelling predicts frailty levels in older adults

Frailty is characterized by a persistent and progressive decline in physiological reserves, leading to increased vulnerability to stressors and a heightened risk of adverse health outcomes, both physically and mentally. Despite frailtys prevalence in older adults, there is limited research on its neural substrates, especially using task-based brain functional connectivity. In this study, we used connectome-based predictive modelling (CPM) to find a linear relationship between task-based connectomes -- taken from tasks that involved similar handgrip manipulations -- and a separate measure of frailty: the maximum grip strength in older adults. We observed that the task-based connectomes were able to explain individual differences in grip strength, with the Subcortical and Cerebellum network, particularly the caudate nucleus, functional connectivity being the strongest predictor. These findings demonstrate that task-based functional connectomes can serve as personalized markers that can predict individual behavioral measures, including handgrip strength, and point to involvement of the caudate nucleus in frailty. Key pointsO_LIWe used connectome-based predictive modeling on task-based fMRI to predict grip strength in older adults, a key marker of physical frailty. C_LIO_LIThe model significantly explained inter-individual differences in contraction strength using functional connectivity patterns. C_LIO_LISubcortical regions, especially the caudate nucleus, played a major role in prediction, highlighting their relevance in frailty assessment and potential interventions. C_LI

neuroscience↗

bioRxiv2025

Dynamic fingerprinting of the human functional connectome

Resting-state functional connectivity (FC) have distinct, personalized patterns that could serve as a unique fingerprint of each individuals brain. While previous brain fingerprinting methods have used functional connectivity maps over a scanning session (static method), it has been shown that the brain is a dynamic system that switches between several metastable states, each of which having a different FC map. Taking the dynamic nature of brain connectivity into account will likely lead to more subject-specific information and better individual identification. In this paper, we derived the state-specific FCs using sliding window correlation and clustering and evaluated their performance in individual identification and cognitive score prediction. The resultant dynamic fingerprints outperformed the static fingerprints in identification accuracy. Furthermore, some of the brain states were more accurate in predicting cognitive scores, indicating that connectivity in some brain states is informative of cognition abilities, possibly useful as biomarkers for brain disorders. Impact StatementOur findings suggest that state-specific functional connectivity patterns of the brain are unique for each individual. These brain states predicted participants cognitive performance, suggesting that they have the potential to be used as biomarkers for cognitive function or neurological disorders. Integrating state-based functional connectivity into clinical frameworks could potentially enhance early diagnosis and patient stratification, and lead to targeted interventions for neuropsychiatric and neurodegenerative conditions.

neuroscience↗

bioRxiv2023

Autophagy mediates cancer cell resistance to doxorubicin induced by the Programmed Death 1/Programmed Death Ligand 1 immune checkpoint axis

BackgroundWhile the Programmed Death 1/Programmed Death Ligand 1 (PD-1/PD-L1) immune checkpoint is an important mechanism of immune evasion in cancer, recent studies have shown that it can also lead to resistance to chemotherapy in cancer cells via reverse signaling. Here we describe a novel mechanism by which autophagy mediates cancer cell drug resistance induced by PD-1/PD-L1 signaling. MethodsHuman and mouse breast cancer cells were treated with recombinant PD-1 (rPD-1) to stimulate PD-1/PD-L1 signaling. Activation of autophagy was assessed by immunoblot analysis of microtubule-associated protein 1A/1B-light chain 3 (LC3)-II and Beclin 1 protein levels, two important markers of autophagy. Moreover, autophagosome formation was assessed in human breast cancer cells using green fluorescence protein (GFP)-tagged LC3. Cells were either treated with Beclin 1 or Atg7 shRNA to assess the role of autophagy on resistance to doxorubicin mediated by PD-1/PD-L1 signalling. We then investigated signaling mechanisms upstream of PD-1/PD-L1 induced autophagy by assessing phosphorylation of extracellular signal-related kinase (ERK). ResultsTreatment of cells with rPD-1 resulted in a time-dependent increase in LC3-II as well as Beclin 1, and an increase in autophagosome formation. Knockdown of Beclin 1 or Atg7 prevented drug resistance induced by PD-1/PD-L1 signaling. Exposure of breast cancer cells to rPD-1 resulted in increased ERK phosphorylation and inhibition of ERK activation abolished autophagy induced by PD-1/PD-L1 signaling. ConclusionsThese studies provide a rationale for the use of PD-1/PD-L1 immune checkpoint blockers and autophagy inhibitors as potential chemosensitizers in cancer therapy.

cancer biology↗

bioRxiv2021

Generalizability in White Blood Cells' Classification Problem

Counting and classifying white blood cells (WBCs) in blood samples helps the early diagnosis of the disease. Many works have been done to develop machine learning-based methods to count WBCs. However, most of these works have low generalizability, and their accuracy decreases sharply as the dataset changes. In this paper, a new method is presented that helps to increase the generalization power. In this method, first, the WBCs nucleus is segmented, and then its convex hull is obtained. By subtracting the nucleus from the convex hull, a new image is created called the representative of the convex hull (ROC). Then, by Training a convolutional neural network (CNN) with the cells RGB image as well as the binary images of the nucleus and ROC, the generalization power is increased. The proposed method was first trained on the Raabin-WBC dataset, then its performance was evaluated on the LISC dataset without retraining. The proposed methods accuracy on the Raabin-WBC and LISC datasets is 93.97% and 51.57 %, respectively. Besides, the generalization power of four well-known CNNs named VGG16, ResNext50, MobileNet-V2, and MnasNet1 was investigated. It was found that VGG16 has more generalization power among these models.

bioengineering↗

bioRxiv2021

New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

bioengineering↗